■溃疡性结肠炎(UC)是一种影响直肠和结肠的终身炎症性疾病,有许多治疗选择,需要个性化的治疗计划。组蛋白修饰调节染色体结构和基因表达,对炎症和免疫反应产生影响。然而,组蛋白修饰相关基因与UC之间的关系尚不清楚.
■来自GSE59071和GSE66407的转录组数据是从基因表达综合(GEO)获得的,包括处于发炎和非发炎状态的UC患者的结肠活检表达谱。差异表达基因(DEG)分析,功能富集分析,加权基因共表达网络分析(WGCNA),和随机森林用于鉴定与UC炎症相关的组蛋白修饰相关核心基因。通过最小绝对收缩和选择算子(LASSO)和支持向量机-递归特征消除(SVM-RFE)筛选特征,利用logistic回归建立分子炎症预测模型。该模型在GSE107499数据集中进行了验证,并使用受试者工作特征(ROC)和校准曲线评估特征的性能。对接受英夫利昔单抗治疗的UC患者的结肠活检组织进行免疫组织化学(IHC)染色,以进一步证实其临床应用价值。GSE14580的单变量逻辑回归突出了与英夫利昔单抗反应相关的特征。
■在患有UC的炎性和非炎性患者之间确定了总共253个组蛋白修饰相关的DEGs。七个关键基因(IL-1β,MSL3,HDAC7,IRF4,CAMK2D,使用WGCNA和随机森林选择AUTS2和PADI2)。通过单变量逻辑回归,三个核心基因(CAMK2D,AUTS2和IL-1β)被进一步纳入以构建分子炎症预测模型。在独立验证数据集中,模型的曲线下面积(AUC)为0.943。观察到CAMK2D蛋白表达与英夫利昔单抗反应之间存在显著关联,在GEO数据库的GSE14580的另一个独立验证集中进行了验证。
■基于CAMK2D的分子炎症预测模型,AUTS2和IL-1β可以可靠地区分UC患者的黏膜炎症状态。我们进一步揭示了CAMK2D是英夫利昔单抗反应的预测标志物。这些发现有望为UC患者的个性化治疗和管理策略提供新的证据基础。
Ulcerative colitis (UC) is a lifelong inflammatory disease affecting the rectum and colon with numerous treatment options that require an individualized treatment plan. Histone modifications regulate chromosome structure and gene expression, resulting in effects on inflammatory and immune responses. However, the relationship between histone modification-related genes and UC remains unclear.
Transcriptomic data from GSE59071 and GSE66407 were obtained from the Gene Expression Omnibus (GEO), encompassing colonic biopsy expression profiles of UC patients in inflamed and non-inflamed status. Differentially expressed gene (DEG) analyses, functional enrichment analyses, weighted gene co-expression network analysis (WGCNA), and random forest were performed to identify histone modification-related core genes associated with UC inflammation. Features were screened through the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), establishing a molecular inflammatory predictive model using logistic regression. The model was validated in the GSE107499 dataset, and the performance of the features was assessed using receiver operating characteristic (ROC) and calibration curves. Immunohistochemistry (IHC) staining of colonic biopsy tissues from UC patients treated with infliximab was used to further confirm the clinical application value. Univariate logistic regression on GSE14580 highlighted features linked to infliximab response.
A total of 253 histone modification-related DEGs were identified between inflammatory and non-inflammatory patients with UC. Seven key genes (IL-1β, MSL3, HDAC7, IRF4,
CAMK2D, AUTS2, and PADI2) were selected using WGCNA and random forest. Through univariate logistic regression, three core genes (
CAMK2D, AUTS2, and IL-1β) were further incorporated to construct the molecular inflammatory predictive model. The area under the curve (AUC) of the model was 0.943 in the independent validation dataset. A significant association between
CAMK2D protein expression and infliximab response was observed, which was validated in another independent verification set of GSE14580 from the GEO database.
The molecular inflammatory predictive model based on
CAMK2D, AUTS2, and IL-1β could reliably distinguish the mucosal inflammatory status of UC patients. We further revealed that
CAMK2D was a predictive marker of infliximab response. These findings are expected to provide a new evidence base for personalized treatment and management strategies for UC patients.